Bladder cancer (BLCA) is the ninth most common malignancy worldwide and the fourth most common cancer in men. Copper levels are significantly altered in patients with thyroid, breast, lung, cervical, ovarian, pancreatic, oral, gastric, bladder, and prostate cancers. Outcomes can be predicted by constructing signatures using lncRNA-related genes associated with outcomes.We identified lncRNAs related to outcomes, those differentially expressed in bladder cancer, and cuproptosis-related lncRNAs from TCGA. We identified the intersection to obtain 12 genes and established a prognostic risk signature consisting of eight genes using LASSO-penalized multivariate Cox analysis. We constructed a training set, performed survival analysis on the high-and low-risk groups, and performed validation in the test and full sets. There existed a substantial contrast in the likelihood of survival among the cohorts of high and low risk. An in-depth analysis of the gene mutations associated with tumors was conducted to evaluate the risk of developing cancer. We also performed gene analysis on neoadjuvant chemotherapy. We conducted experimental validation on the key gene UBE2Q1-AS1 in our prognostic signature.The risk signature we constructed shows significant differences between the high-risk group and the low-risk group. Univariate survival analysis of the eight genes in our signature showed that each gene distinguished between high- and low-risk groups. Sub-group analysis revealed that our risk score differed significantly in tumor stage, age, and gender. The analysis results of the tumor mutation burden (TMB) showed a significant difference in the TMB between the low- and high-risk groups, which had a direct impact on the outcomes. These findings highlight the importance of TMB as a potential prognostic marker in cancer detection and prevention. We analyzed the immune microenvironment and found significant differences in immune function, validation responses, immunotherapy-related positive markers, and critical steps in the tumor immunity cycle between the high- and low-risk groups. We found that the effect of anti-CTLA4 and PD-1 was higher in the high-risk group than in the low-risk group.Gene analysis of neoadjuvant chemotherapy revealed that the treatment effect in the high-risk group was better than in the low-risk group. The key gene UBE2Q1-AS1 in our prognostic signature can significantly influence the cell viability, migration, and proliferation of cancer cells.We established a signature consisting of eight genes constructed from cuproptosis-related lncRNAs that have potential clinical applications for outcomes prediction, diagnosis, and treatment. As the 2.2.1.p < 0.05), 752 differentially expressed lncRNAs  and 1492 cuproptosis-related lncRNAs . We collected gene expression data from 428 TCGA-BLCA samples and clinical data of 413 patients from TCGA-BLCA. We normalized the differentially expressed gene data using log2 (exp + 1). These rigorous data analysis methods allowed us to obtain accurate and reliable results. The lists of outcomes-related lncRNAs, differentially expressed lncRNAs, and cuproptosis-related lncRNAs intersected and then we obtained 12 genes, which were used for subsequent analysis.Our study utilized data from TCGA2.2.Using the expression of 12 genes, we employed Least absolute shrinkage and selection operator (LASSO) regression analysis to develop the cuproptosis-related lncRNA signature. We constructed the risk score using the following formula: (Coefficient lncRNA1 × expression of lncRNA1) + (Coefficient lncRNA2 × expression of lncRNA2) + ν + (Coefficient lncRNAn × expression lncRNAn), and applied it to each patient. Based on the best cut-off value of the risk score (0.036391179), we classified patients into high- and low-risk groups. To ensure robust and reliable results, we utilized strict and validated methods in our data analysis approach.2.3.We utilized Sangerbox2.4.We downloaded tumor mutation burden (TMB)-related data from TCGA and used the R package ‘TMBcor’ to analyze the correlation of various TMB data with risk scores.2.5.We used the ESTIMATE algorithm to calculate stromal and immune scores based on gene expression data, providing insights into the tumor microenvironment. The correlation analysis of staging and inflammatory factors with clustering was performed using the ‘heat map’ R package. We also tested the relationship between our risk score and immune checkpoints. The correlation between risk score and immune cells, immune process, and critical steps of the cancer immunity cycle were identified using the ‘pheatmap’ and ‘riskImmCor’ R packages.2.6.Sensitive immunotherapeutic and neoadjuvant chemotherapy targets were identified based on the gene expression matrix using the R package ‘pRRophetic.’ The R package ‘ggpubr’ was used to draw boxplots of sensitive targets to compare the correlations with risk scores. We compared the risk scores of RB1, ARID1A, ERBB2, ATM, ERCC2, and FANCC between high- and low-risk groups and the mutation burden of these factors that participate in the neoadjuvant chemotherapy process. We also compared the associations of BLCA-associated drug target genes identified from the Drugbank database with our constructed risk signature for targeted and radiation therapies. We analyzed immunotherapy, ERBB therapy, chemotherapy, and anti-angiogenic therapy effects using the Drugbank database.2.7.2. UBE2Q1-AS1-targeted small interfering RNA (siRNA) was purchased from JTSBIO Co. (China) to transfect the cells. The UBE2Q1-AS1 siRNA sequence was as follows:The UM-UC3 cell line was purchased from the Cell Bank of the Chinese Academy of Sciences (China). Cells were cultured in DMEM (high glucose) medium (Hyclone) containing 10% fetal bovine serum (FBS) at 37°C and 5% COGGAGAAACCUGAAUCAUUACAUCTCGAGAUGUAAUGAUUCAGGUUUCUCCUU.2.8.® Premix Ex TaqTM kit . The results were analyzed on a Thermal Cycler Dice ™ Real-Time TP800 System , and GAPDH was selected as the internal reference gene. The relative gene expression level was obtained using the ΔΔCT method.The cells were subjected to RNA extraction using RNAiso Plus  for the isolation of total RNA. Subsequently, the reverse transcription of total RNA was conducted employing the Prime Script RT Master Mix . The resulting cRNA was subjected to qRT-PCR using the SYBR The primer sequences were as follows: UBE2Q1-AS1 .2.9.96-well plates were utilized for seeding UM-UC3 cells, and the Cell Technology Kit-8 (CCK-8) assay reagent (Dojindo Molecular Technologies) was added as per the prescribed guidelines of the manufacturer. Subsequently, an absorbance plate reader (Bio-Rad) was employed to measure the absorbance at a wavelength of 450 nm.2.10.Transwell chambers with 8-μm pores were inserted into 24-well plates . Each well received 600 μL of DMEM medium containing 10% FBS, while each chamber was loaded with 200 μL of FBS-free DMEM medium containing 10,000 suspended cells. Following incubation for 48 h at 37°C in 5% CO2, the inserts were washed with phosphate-buffered saline (PBS), and the membrane undersides were stained with crystal violet for 15 additional minutes. Cell passage through the membrane was measured using ImageJ installed on an inverted microscope  and images were captured at 10 × magnification.2.11.We performed a wound-healing assay in six-well plates. When UM-UC3 cells grew to a confluence higher than 90%, we scratched a straight artificial wound in the plate using a 200-μL pipette tip and incubated it with an FBS-free medium. After 24 h, cell migration was observed and photographed under a 10x lens using a microscope as previously described. Migration rate statistics were performed using ImageJ.2.12.Cell proliferation was evaluated using an EdU assay kit . In brief, 2000 transfected cells were seeded into 96-well plates. After 18 h, 500 μL of medium containing 50 μM EdU was added to each well, followed by incubation for 3 h. The medium was then removed, and the cells were fixed with 4% paraformaldehyde at room temperature for 15 min. Next, 0.3% Triton X-100 in PBS was added to each well to permeabilize the cells for 15 min, and a click reaction solution was added for an additional 30 min. After staining with Hoechst 33342 diluted in PBS (1:1000) for 10 min, the proportion of EdU-positive cells was determined using a fluorescence microscope , and images were captured for subsequent analyses with ImageJ software .3.3.1.We found 2418 lncRNAs associated with BLCA outcomes, 752 differentially expressed lncRNAs, and 1492 lncRNAs associated with cuproptosis using the R package. Twelve genes were obtained by the intersection of the three groups of lncRNAs . Based oThe expression levels of the eight genes were significantly different between BLCA and normal tissues ,E. The rWe obtained data from TCGA and used them to build a training set to calculate the risk score. All BLCA patients were randomly allocated into two sets using a complete randomization method at a 1:1 ratio through SPSS software. One set served as the training set, while the other set served as the test set. The risk score had a positive relationship with the number of deaths. The survival curve demonstrated a marked contrast in 5-year overall survival (OS) between the high-risk and low-risk cohorts, revealing significant differences . A heat Subsequently, we conducted single-gene survival analysis for the eight genes in the full set of samples, revealing significant segregation of the high-risk and low-risk groups for each individual gene –H.Furthermore, a single-factor survival analysis was conducted on both the training and test sets, effectively discerning the high-risk group from the low-risk group in a significant manner .Univariate and multivariate Cox regression analyses were conducted, revealing that age, stage, and risk score were independent risk factors in predicting outcomes .We analyzed cancer stages in the high- and low-risk groups and analyzed the risk scores of patients in the Stage 1 + Stage 2 and Stage 3 + Stage 4 groups. There was a positive correlation between stage and risk scores ,B. We peThese comparisons revealed significant differences between these sub-groups. We performed the same clinical sub-groups survival analysis in the training and test sets and found significant differences . These f3.2.We analyzed the tumor-related gene mutation burden in the samples of the low- and high-risk groups and significant differences in the gene burden, gene type, and mutation ,B. The d3.3.We analyzed the immune microenvironment of samples with different risk scores and obtained the expression profiles of their immune microenvironments . We thenBased on our analysis of immune checkpoints, the high- and low-risk groups showed a significant difference, and we focused on CTLA4 and PD-1 (targets for cancer immunotherapy). We divided the samples according to treatment regimens: no anti-CTLA4 and PD-1, only anti-CTLA4, only anti-PD-1, and anti-CTLA4 and PD-1 combined. Our study compared the treatment effect scores of anti-CTLA4 or PD-1 treatment regimens between high- and low-risk groups. The results indicated that patients in the high-risk group who received single or combined anti-PD-1 checkpoint therapy showed significantly better treatment effects than those in the low-risk group. This finding suggests that the immunotherapy effect of anti-CTLA4 and PD-1 in the high-risk group was better than that of the low-risk group –D. The f3.4.We first knocked down UBE2Q1-AS1 using siRNA . Subsequ4.Studies on lncRNA function revealed many mechanisms, and it is currently believed that lncRNAs are inextricably related to cellular functions such as differentiation, apoptosis, and proliferation . As a no5.We analyzed the genes associated with cuproptosis-related lncRNAs and constructed an eight-genes signature for BLCA, which has value in predicting BLCA outcomes and selecting targeted therapy or neoadjuvant chemotherapy.The original contributions presented in the study are included in the article/JS, MD, SL, LW, and JB designed the study. JS and LW analyzed and wrote the manuscript. MD provided partial code support, as well as made constructive modifications to the article. SL has made contributions to the code modifications and uploads during the editing process. All authors read and agreed to the final version of the manuscript.This study was supported by the National Natural Science Foundation of China (Grant No. 82172568).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Highly sensitive and reproducible analysis of samples containing low amounts of protein is restricted by sample loss and the introduction of contaminants during processing. Here, we report an All-in-One digital microfluidic (DMF) pipeline for proteomic sample reduction, alkylation, digestion, isotopic labeling and analysis. The system features end-to-end automation, with integrated thermal control for digestion, optimized droplet additives for sample manipulation and analysis, and an automated interface to liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Dimethyl labeling was integrated into the pipeline to allow for relative quantification of the trace samples at the nanogram level, and the new pipeline was applied to evaluating cancer cell lines and cancer tissue samples. Several known proteins  and pathways were observed between model breast cancer cell lines related to hormone response, cell metabolism, and cell morphology. Furthermore, differentially quantified proteins  were found in comparisons of healthy and cancer breast tissues, suggesting potential utility of the All-in-One pipeline for the emerging application of proteomic cancer sub-typing. In sum, the All-in-One pipeline represents a powerful new tool for automated proteome processing and analysis, with the potential to be useful for evaluating mass-limited samples for a wide range of applications. The All-in-One digital microfluidic pipeline allows for end-to-end automation of processing and analysis of mass-limited proteomic samples. State-of-the-art analysis techniques can identify such peptides when present in zeptomole quantities.3,4Mass spectrometry (MS)-based proteome profiling has emerged as a powerful, unbiased technology for identification and quantitation of the set of expressed proteins found in complex biological samples.5–7 More recently, methods that integrate ion mobility spectroscopy (IMS) in-line with traditional MS approaches have pushed this record to thousands of proteins per cell.8–11 Another trend in bottom-up proteome profiling for applications in biomedicine is the development of strategies in which samples are differentially labeled with mass-tags, to allow for semi-quantitative comparisons between them.12–14 These techniques, which are increasingly being applied to clinical cancer typing,15,16 typically prioritize reproducible detection over the absolute number of proteins identified. The current report falls squarely in this category of application.There are many exciting current trends in bottom-up proteome profiling for applications in biomedicine. One important trend focuses on pushing the boundary for how many proteins can be identified from tiny samples, such as the lysate generated from a single mammalian cell. As recently as 3–5 years ago, the record for such studies was a few hundred proteins per cell.17,18 Specifically, the number of steps, the number of reaction vessels, and the process of transferring the sample from a reaction system to the high performance liquid chromatography-mass spectrometry (HPLC-MS) system are all known to increase the probability of non-specific adsorption, sample loss and contamination.19,20 Numerous sample preparation technologies for mass-limited proteomic samples have been developed to address this challenge, including SP3,21 SODA,22 nanoPOTS,5 and on-column techniques.23 While these are important developments, the operation requirements are typically quite high, such as access to robotic nanoliter liquid handling or complex capillary and column connections, which are not accessible to all analysts (even in well-equipped labs).Whether a particular study is focused on label-free identification of large numbers of proteins per cell, or label-enabled semi-quantitative comparisons between samples, all bottom-up proteome profiling techniques suffer from inevitable sample loss that occurs during the extensive sample processing that is required prior to analysis.24 DMF has been viewed for many years as being a useful tool for processing samples upstream of analysis by mass spectrometry and proteomics.25,26 The smaller volumes in DMF systems provide some advantages for reactions (including rapid heat transfer into and out of the system), and the enclosed system and automated control of DMF reduces the probability of experimental variation and contamination that can be introduced during manual operations. DMF has previously been used in proteomics research to extract analytes from single cells,7 to extract proteins from mixtures by precipitation,27 immunodepletion,28 and immunoenrichment,29 as well as to implement protein digestion and related processes,30–36 and mass-tag labeling.37 These techniques represent important steps forward for the field, but none are fully integrated and automated – in each of these examples, there are still many manual transfers required, which takes away from some of the potential advantages of the technology.One potential solution to the challenges indicated above is automated processing by digital microfluidics (DMF), a miniaturized fluid-handling technique that manipulates samples and reagents as picoliter-to microliter-sized droplets, typically by application of electrodynamic forces on an array of electrodes, for automated sample processing and reactions.Here, we describe the first fully integrated and automated system for digital microfluidic proteome sample handling and analysis, to facilitate what we call the “All-in-One” DMF pipeline for proteomic sample processing. In developing this system, we encountered numerous challenges, including: (a) complexities in ensuring temperature control for digestion and other steps, (b) incompatibility of DMF droplet constituents with HPLC-MS analysis, and (c) efficient sampling between DMF and HPLC-MS. Here we describe our solutions to these challenges, realizing a fully integrated process and analysis system. Importantly, the system was applied to the differential analysis of human breast cancer tissue samples, demonstrating its potential use for clinical cancer sub-typing. We propose that the All-in-One pipeline may be appropriate for a wide range of applications going forward.22.138 liquid processing unit (version 3.0) running MicroDrop software (version 2.31.1) to control droplet movement . In most DMF/proteomic analyses described previously,7,28–37 processed samples were manually extracted from the device and then transferred to the autosampler/HPLC (risking sample loss and cross-contamination). The one exception is a previous report20 of a direct interface between DMF and an HPLC autosampler. Here, we introduce an improved DMF-autosampler interface , while others do not.44–46 Unfortunately, the surfactants that are often used in DMF (including pluronics and tetronics), interfere with peptide detection of MS. Therefore, the choice of surfactant and its concentration for a proteomic workflow on DMF is critical. In previous (independent) reports, we7 and Tholey and co-workers37 have found success in using maltoside derivatives that are known to be useful for proteomics [including n-dodecyl-β-d-maltoside (DDM) and 3-dodecyloxypropyl-1-β-d-maltopyranoside (DDOPM)] as additives for droplet manipulation on DMF devices. In our previous report,7 the optimum working concentration of DDM was not thoroughly investigated, and in fact, a high concentration/high viscosity/low velocity solution of 0.0125% wt/wt was used. Thus, a primary goal for our work here was to rationally assess and optimize the concentration of DDM for analysis.Surfactants are important in bottom-up proteome profiling, as the addition of an appropriate surfactant can improve recovery by reducing non-specific protein adsorption to surfaces, as well as aiding in enhancing digestion efficiency.47 were generated for four concentrations of DDM  to identify optimum working conditions. As illustrated in −1). This concentration, which is substantially lower than those reported previously,7,37 was used for all subsequent experiments, allowing for rapid and efficient DMF operations.Force–velocity curves2.430–36 or mass tag labeling,37 but never both, and never before have either set of methods been combined with automated loading into HPLC-MS. Here, we set out to develop a method that does all of these things, which we dub the “All-in-One” DMF proteome analysis pipeline.Bottom-up proteomic sample processing requires the execution of a long list of repetitive and iterative reaction-steps , while maintaining careful control over temperature, pH, and light level. This complexity is exacerbated when mass-tag labeling is included, which requires that multiple replicates of each sample (exposed to different tags) be carried through the same set of steps in parallel. The DMF community has previously demonstrated the feasibility of on-chip proteomic reactions38 control system used here was modified to include two automated heating units – an auxiliary heating unit (introduced here for the first time) was used to denature the sample at 70 °C during disulfide reduction by exposure to TCEP  was used to incubate the sample at 37 °C during enzymatic digestion by exposure to trypsin, after an alkylation reaction by exposure to IAA  and sodium cyanoborohydride (NaBH3CN and NaBD3CN), and then the reactions are quenched by exposure to hydroxylamine. Finally, samples are acidified by exposure to formic acid and loaded into the autosampler via the manifold described above , resulting in fewer missed cleavages. To test this hypothesis, a mixture of 5 protein standards was digested on DMF and in tubes. As shown in Fig. S6,2.515,16 an emerging technique in which isotopically labeled proteomes are compared to identify protein expression patterns that correlate with different forms and stages of metastatic cancer. As an intermediate step toward this goal, two breast cancer cell lines, MCF-7 and MDA-MB-231, were evaluated using the All-in-One pipeline. These samples were selected because they are known to have dramatic differences in cell invasion behavior, with MDA-MB-231 cells being more invasive into a collagen-I-rich matrix, a proxy for aggressive metastatic disease.52The ultimate goal of this work was to develop a technique useful for cancer sub-typing,12). Three replicates (or “batches”) of labelled samples were evaluated using the All-in-One pipeline. The correlation coefficients between protein intensities from channels L and H in batches 1, 2, and 3 were 91.5, 95.4, and 97.3%, respectively, confirming the repeatability of the measurements. A total of 973 proteins were identified across the three channels tested  isotope, while MDA-MB-231 cells were split and labeled with light (L) or heavy (H) isotopes. Assigning two channels to the same cell line (MDA-MB-231) allows them act as controls for one another as well as to serve as a reference to compare against the experimental MCF-7 channel  allows for assessments of differential expression with high confidence.12Finally, a heat map of the 597 quantitated proteins  is shown in 53 (ii) metabolic strategies differ between the cell types ; and (iii) MCF-7 cells are more epithelial in morphology, while MDA-MB-123 cells are more mesenchymal.56,57Armed with a robust and reproducible comparative dataset, attention was turned to codifying the specific differences in protein expression between the two cell lines. From previous studies, we expected to observe three main distinctions: (i) MDA-MB-231 cells do not respond to hormones, whereas MCF-7 cells may;z-score) protein intensities, distinct blocks of protein expression were found that confirmed our three key expectations. That is: (i) hormone pathway related proteins like HSP90AB1 and HSPB1/HSP27 (highlighted in aqua), are upregulated in MCF-7 cells;58 (ii) glycolysis-related proteins LDHA, ENO1 and PGK1 (highlighted in pink) are more abundant in MDA-MB-231 cells, and (iii) other proteins related to the epithelial morphology of MCF-7 (including KRT18 and AKR1C2) are among the most variable of the proteins.A heat map highlighting the expression of the 118 most variable proteins between the two cell lines is shown in 59–61 and carboxylic acid biosynthetic process . The latter is related to the tricarboxylic acid (TCA) cycle, which has a prominent role in supplying the oxidative phosphorylation pathway.62 Likewise, pathways that were more active in MDA-MB-231 cells included those involved with glycolysis  and actin myosin filament sliding , the second of which is known to be more plentiful in the mesenchymal-like MDA-MB-231 cells.56 In sum, the differential pathway analysis supported the findings in the differential protein expression analysis, giving us confidence to move to evaluating clinical samples.The pathways represented by the 118 variably expressed proteins were then determined using Enrichr59–61. Pathway2.615,16 Three samples were acquired: healthy breast tissue (CP565563), a small breast cancer tumour that was either not metastatic or was collected early in the disease progress (CP531533), and a large, metastatic breast cancer tumour (CP629057). Upon evaluating the three samples with the All-in-One pipeline, 882 proteins were identified in total, and 436 proteins were quantified . Among this group, 31 proteins /non-invasive (MCF-7) cancer cell line study, including A0A1B0GW44 (CTSD) and K1C18 (KRT18). They are known promotors of aggressive tumour growth,79,80 which suggests that the All-in-One pipeline may be a valuable tool for correlating cellular function  to in vivo progression of disease.Intriguingly, 27 proteins were found to be differentially quantified in a comparison between the two cancer samples Table S3. For exa5,21–23 that are being developed to address the problem of processing mass-limited samples with minimal exposure to surfaces. We propose that the All-in-One DMF pipeline is a useful addition to this list, bringing the combination of flexibility  with end-to-end automation to bear. We anticipate that the proof-of-concept application described here will be the first of many to leverage these unique properties.The data presented here demonstrate the potential utility of the All-in-One DMF pipeline for cancer sub-typing in clinical samples. These types of samples are by nature precious and mass-limited, and thus are particularly susceptible to the problem of analyte loss during processing. As described in the introduction, there are a number of emerging technologies3We have introduced an All-in-One DMF proteomic pipeline for digestion, isotopic labeling, and loading of miniaturized proteomic samples. Through the multiplexed control of DMF, up to three samples in the current system  can be processed simultaneously and then (after isotopic labeling) combined for analysis. By integrating localized heating and a direct-loading interface for HPLC-MS, user intervention is minimized, and the risk of sample loss and contamination is reduced. The pipeline has been applied to cancer sub-typing using model cell lines and clinical tissue samples. The system as described is appropriate for semiquantitative comparisons between different samples, and if in the future it can be combined with IMS, it may someday be appropriate to apply to cutting-edge single-cell protein identification experiments. In sum, we propose that the All-in-One DMF pipeline is a powerful new tool for automated proteome processing and analysis, with the potential to be useful for evaluating mass-limited samples for a wide range of applications.44.1n-dodecyl-β-d-maltoside (DDM), tris(2-carboxyethyl)phosphine (TCEP), iodoacetamide (IAA), triethylammonium bicarbonate (TEAB), trypsin, and protein standards were obtained from Sigma-Aldrich . Lys-C was purchased from Promega .Cell media reagents, acetonitrile (ACN), formic acid, and water (LC/MS-grade) were purchased from Thermo Fisher Scientific . Dimethyl labeling reagents, 4.2DMF devices comprising (i) bottom plates bearing 68 roughly square (2.2 × 2.2 mm) driving electrodes (where 60 of them formed a 4 row × 15 column electrode array) and 12 reservoir electrodes, and (ii) 25 × 75 mm top plates bearing a counter-electrode, were fabricated as described previously. Each top plate was modified by drilling a 2 mm dia. Micro-hole in the center of the piece (relative to the short axis) and approximately 23 mm from one edge  using a micro-drill press. Top plates and bottom plates were assembled with ∼190 μm-thick inter-plate spacers formed from two pieces of Scotch double-sided tape (3M), such that the access hole was positioned over row 2 and between columns 2–3 in the driving electrode array. This spacer defines “unit droplets” (with volume sufficient to cover a single driving electrode) of approx. 0.9 μL.38 .48,49 The DropBot and the built-in heater unit were controlled using a custom version of MicroDrop software , while the auxiliary unit was controlled by a separate interface . Devices were interfaced to the DropBot system through a pogo-pin connector, and electrodes were actuated in pre-programmed steps which allowed droplet dispensing, moving, and mixing by applying  100 VRMS as square waves at 10 kHz (parameters optimized as described below).Devices were controlled by using a custom version of the open-source DropBot system4.3An ESP32 Feather Board  was used to control a thermoelectric unit  through a VNH5019 motor driver . The thermoelectric unit was sandwiched between an aluminum plate (1.8 mm × 44 mm × 88 mm)  and a heatsink/fan unit . The fan was connected to the VHN5019 motor driver, and was programmed to turn on during cooling cycles and off during heating cycles. Thermal paste  was applied on either surface of the thermoelectric unit to ensure good thermal conductivity with the aluminum plate and the heatsink. The ESP32 board was also connected to two Negative Temperature Coefficient (NTC) thermistors  which were placed in close proximity to the thermoelectric unit to provide real-time temperature feedback. Pieces of Delrin (6 mm × 46 mm × 20 mm)  were placed on either end of the heating board to protect and allow the user to move the board when it was hot. A magnet was affixed in one of the two Delrin pieces and was used to align and secure the board in place during operation.48,49 to output both positive and negative values to facilitate heating and cooling respectively. The thermistor closest to the thermoelectric unit was used for feedback while the thermistor further away from the unit served as a monitor of the heat distribution over the heating block. The proportional term (Kp), integral term (Ki) and derivative term (Kd) of the PID controller were tuned to 70 °C, and the maximum temperature was set by software to be 120 °C.Custom firmware was developed in C++ to allow the ESP32 board to communicate with and be controlled by the host computer over USB. The firmware implemented a proportional–integral–derivative (PID) algorithm for the fine control of the heating unit using the temperature readings from the thermistors. The PID algorithm was modified from previous versionsCustom software was developed in Python to communicate with the ESP32 board and control the heating unit. Using this software, a custom heat profile could be programmed where desired temperatures and heating profiles could be specified. The software also logged the temperature readings from both thermistors on the heating board.4.4A DMF-HPLC manifold was fabricated from poly-lactic acid filament  in two parts using an Ultimaker 2 3D printer . Steel dowel pins  were used to align the top and bottom components and to position the device in the autosampler. The overall manifold dimensions are 144 mm L × 90 mm W × 33 mm H. An STL file for the design is included in the ESI.†4.547 force–velocity curves were generated to identify optimal working conditions. Briefly, double-unit droplets were dispensed onto the array and velocity was monitored as a function of driving force; each condition was repeated in triplicate. In subsequent experiments, 100 VRMS driving voltage was used (corresponding to ∼30 μN mm−1).Aqueous solutions of TEAB (50 mM) were supplemented with DDM to final concentrations ranging from 0.00125% to 0.005% wt/wt. Using a modified version of a method described previously,4.6−1 in 0.0025% wt/wt DDM in 50 mM aqueous TEAB. The protein mixture solution was prepared by dissolving 1 mg of each of the proteins in 1 mL of DI water, and then diluting them to 1 or 10 ng μL−1 (of each protein) in 0.0025% wt/wt DDM in 50 mM aqueous TEAB.The proteins evaluated included a BSA standard and a protein standard mixture . The former was prepared by dissolving 1 mg of BSA in 1 mL of DI water, and then diluted to 1 ng μL4.7−1) was loaded into a reservoir, and three unit-droplets were dispensed onto the array and merged with the alkylated protein droplets at 37 °C , CD2O (medium), or 13CD2O (heavy), were loaded into reservoirs, and one unit-droplet of each was dispensed and mixed with one of the sample droplets. (6) Aliquots (37.5 mM) of sodium cyanoborohydride (NaBH3CN) and sodium cyanoborodeuteride (NaBD3CN) were loaded into reservoirs, and unit droplets of the former were merged with the light- and medium-labeled samples, while a unit droplet of the latter was merged with the heavy-labeled sample. All samples were incubated for 30 min. (7) An aliquot of ammonium hydroxide (0.2% w/v) was loaded into a reservoir, and three unit-droplets were dispensed and merged with the sample droplets. (8) The three sample droplets were merged to form one pooled droplet, and then an aliquot of FA (10% w/v) was loaded into a reservoir, and one unit-droplet was dispensed and merged with the pooled droplet. (9) Finally, the merged droplet was moved to the micro-hole to be loaded onto the autosampler. Briefly, the device was interfaced with the custom manifold, which was loaded into the EASY-nLC 1200 ultra-high-pressure system, where the sample was aspirated into the sample loop at 3 μL min−1 until (in most cases) the pooled 18 μL droplet was injected. Protein mixtures evaluated in digestion efficiency experiments were processed in an abbreviated version of the pipeline comprising steps 1–4, after which samples were collected by pipette for manual injection into the autosampler. Manually processed samples were prepared using identical volumes and concentrations, but were manipulated by pipettes and microcentrifuge tubes, heated in a thermocycler (Bio-Rad), and finally transferred into conical-bottomed 96-well plates (Eppendorf), prior to loading into the EASY-nLC 1200.Experiments were carried out in 9 steps, all at room temperature with reagents dissolved in 0.0025% wt/wt DDM (in 50 mM aqueous TEAB) unless specified otherwise. (1) Aliquots of protein sample and TCEP (25 mM) were loaded into separate reservoirs on the device. (2a) Three unit-droplets each of sample and TCEP solution  were dispensed onto the array, and each protein/TCEP droplet pair was merged and mixed. (2b) The auxiliary heater (as shown in 4.8−1), and streptomycin (100 μg ml−1) in T-25 culture flasks in a humidified incubator with 5% CO2 at 37 °C. Cells were trypsinized, washed with phosphate-buffered saline (PBS) three times , and then suspended in PBS at densities ranging from 50 000 to 100 000 cells per ml (determined via hemacytometer). 1 μL samples (about 50–100 cells) were aliquoted into tubes and stored at −80 °C until analysis. For analysis, sample tubes were thawed at room temperature and each sample was mixed (by pipette) with 1 μL of 25 mM TCEP in 0.005% wt/wt DDM. Each sample/TCEP mixture was pipetted onto a DMF device (three at a time), after which steps (2b)–(9) were carried out by DMF as above, except the digestion in step (4) was performed with a mixture of 5 ng μL−1 Lys-C and 5 ng μL−1 trypsin.MCF-7 and MDA-MB-231 cells were grown in Dulbecco's Modified Eagle's Medium (DMEM) with 10% fetal bovine serum (FBS), penicillin  of each diluted breast tissue lysate were processed according the standard nine-step All-in-One pipeline, except that the digestion in step (4) was performed with a mixture of 5 ng μL−1 Lys-C and 5 ng μL−1 trypsin, and the volume loaded into the autosampler in step (9) was approximately one third (∼6 μL) of the pooled droplet.Human breast tissue lysates  were purchased from OriGene . Each sample was diluted to 30 ng μL4.10−1. The sample was then eluted into a fused silica microcapillary column , packed in-house with 1.9 μm-diameter reversed phase C18 particles . Mobile phase A  and mobile phase B  were used to generate a linear gradient of 3–30% B for 90 min, followed by a linear increase from 30–45% B for 20 min, then a linear increase from 45–95% B for 1 min and maintaining at 95% B for 14 min. The flow rate was set to 300 nL min−1. A full mass scan collected by the Orbitrap mass analyzer was from m/z 375 to 1575 with a resolution of 120 000, while the automatic gain control (AGC) target was 5 × 105 and maximum injection time was 50 ms. Precursor ions with charges of +2 to +6 were fragmented by using high energy collision with 27% normalized energy at a resolution of 60 000, AGC of 5 × 104, and maximum injection time of 250 ms. Previously selected precursor ions were excluded from further sequencing for 20 s.Experiments were performed on a Q Exactive HF-X mass spectrometer coupled with an EASY-nLC 1200 system (Thermo Fisher Scientific). Each sample was automatically loaded onto a C18 trap column  at a flow rate of 2 μL min4.1139 Briefly, a human protein database downloaded from UniProt (release 2018_09) was used to search the MS/MS spectra, with methionine oxidation and N-terminal protein acetylation set as variable modifications and cysteine carbamidomethylation set as a fixed modification. Multiplicity was set to 3 with dimethLys0 and dimethNter0 as light labeling, dimethLys4 and dimethNter4 as medium labeling, and dimethLys8 and dimethNter8 as heavy labeling. Trypsin was set as specific proteolytic enzyme with maximum 2 missed cleavages for each peptide. The minimum peptide length was set as 6 amino acids and maximum peptide mass was 4600 Da, and the ‘re-quantify’ option was selected. Both peptides and proteins were filtered with a maximum FDR of 0.01. The default settings of MaxQuant were used for all parameters not mentioned, and the resulting protein intensities were used as proxies for the amount of each identified protein.Raw data files were evaluated by MaxQuant (version 1.6.4.0), using methods similar to those described previously.81 to exclude proteins identified as decoys, potential contaminants, or those identified exclusively by one-site modification from analysis. Heat maps of proteins quantitated in all nine samples were generated in Perseus, and settings of FDR < 0.05 and s0 = 0.1 were applied to generate volcano plots comparing the cell lines' proteomes. In analysis two, the set of proteins appearing in the MaxQuant output files of at least three different samples was evaluated, and summed z-scores (for each cell line's expression of that protein) with a difference of at least 1.0 were identified as being differentially expressed, visualized in heat maps generated in gplot.82 Enrichr59–61 was used to interpret analysis two, applying an adjusted p-value < 0.05 and drawing from the KEGG 2021 human database. Some of the Enrichr data  was visualized using REVIGO83 and Cytoscape.84Nine samples were evaluated in cell lysate experiments: six MDA-MB-231 and three MCF-7; these data were subjected to two analyses. In analysis one, MaxQuant output files were evaluated using Perseus,Three replicate sub-samples were prepared from each of the human tissue samples CP565563, CP531533, and CP629057, and the nine sub-samples were treated and analyzed identically to the cell lysate samples (above).https://proteomecentral.proteomexchange.org) via the PRIDE partner repository85 with identifier PXD036982.All data from the cell lysate and human tissue sample experiments were deposited in the ProteomeXchange Consortium (J. P., C. C., and A. R. W. conceived the concept of All-in-One DMF for miniaturized sample analysis. J. P., Shuailong Z., and M. E. O. fabricated the DMF devices. M. E. O. fabricated the manifold. A. A. S., V. R., and J. P. performed the surfactant optimization experiments on DMF. J. P. and C. C. performed the proteomic experiments on DMF. J. P., C. C., Shen Z., Y. H., E. Y. S., and H. P. carried out the proteomics analysis. B. B. L. and M. D. C. performed the cell culture experiments. J. P., C. C., Shuailong Z., E. Y. S., and A. R. W. wrote and edited the manuscript. All authors discussed the results and commented on the manuscript.There are no conflicts to declare.SC-014-D3SC00560G-s001SC-014-D3SC00560G-s002SC-014-D3SC00560G-s003


Posttraumatic anger is a commonly reported emotion among people who have experienced traumatic events. The current study aimed to demonstrate the reliability and validity of the South Korean version of the DAR-5 (DAR-5-K). The DAR-5 is a single scale with 5 items which measures posttraumatic anger. The DAR-5 is composed of five items that measure anger frequency, intensity, duration, aggression, and its interference with social relations.n = 405) and confirmatory factor analysis (n = 409).Data were collected from 814 South Korean adults who had experienced traumatic events and participated in the study and analyzed via the combination of exploratory factor analysis  symptoms, depression, anxiety, and self-esteem. The DAR-5 cut-off score of 12 that was established in the original validation study successfully differentiated high from low scorers with regard to PTSD symptoms, depression, anxiety, and self-esteem.The results confirm that the DAR-5-K is a brief and psychometrically robust measure of anger that can be used to examine South Korean adults who have experienced traumatic events. Anger is a normal emotion that has functional value, such as facilitating physical and psychological resources or promoting perseverance when faced with difficult situations , 2. HoweThe presence of anger has been identified in a variety of populations who have experienced traumatic events, including combat veterans , 5, CambSeveral traumatic events have occurred in South Korea recently. In 2003, 192 people lost their lives and 148 were injured due to a fire set by an arsonist in the Daegu Metropolitan Subway. A six-year follow-up study showed that approximately 46.6% of the individuals who were injured in this accident received a PTSD diagnosis and reported adaptive problems in interpersonal relationships and increased vulnerability to stress . A studyIn 2014, 304 people, 250 of whom were high-school students, lost their lives in one of South Korea’s most distressing maritime tragedies—the Sewol Ferry disaster. Several studies were conducted after the tragedy to identify changes in psychological symptoms among the bereaved families and friends as well as the general public in South Korea, who were indirectly exposed to the event through the media. In addition to the parents of the adolescent victims of the tragedy, the adolescent friends of the deceased students and even middle- and high-school students who did not personally know any of the victims reported emotional changes, which included the emergence of depressive moods and anger –19. In 2Given that a significant number of traumatic events have occurred in South Korea, the need for localized posttraumatic anger studies has been dramatically increasing. In this respect, the lack of cross-culturally validated posttraumatic anger measures is an urgent issue in South Korea. This consequently underscores the importance of a South Korean-validated assessment of posttraumatic anger as an ensuing consequence of various traumatic events, which range from human-induced traumas to natural disasters.Currently, three instruments are widely used as self-report measures of anger: the State Trait Anger Expression Inventory-2 (STAXI-2) , Novaco Although both the STAXI-2 and NAS-PI have been shown to be psychometrically sound measures of anger , 22, theAs a result of previous studies, the DAR-5 has been mainly used to measure anger levels after experiencing various types of traumatic events worldwide. Using DAR-5 from two-time points, Adler et al.  investigAlthough the DAR-5 has proved its utility as a valid instrument for assessing problematic anger reactions, the scale has only been validated in three countries. The validation studies of the DAR-5 were conducted using samples of American college students with a subgroup of trauma-exposed individuals , communiFor examining problematic anger that is associated with functional impairment by utilizing the DAR-5, a cut-off score of 12 on the DAR-5 was established for the normal college student sample . This cuGiven the numerous traumatic national events, which include the Daegu subway fire, Sewol ferry disaster, Pohang earthquake, and the bushfire, that have recently occurred in South Korea and the various subsequent studies on the negative impacts of these traumatic events, a concise and readily accessible scale of anger that has been validated for use in the South Korean population is necessary. However, the South Korean versions of the STAXI-1 (STAXI-1-K) for adults  and adolThe present study primarily aimed to demonstrate a factor structure of the DAR-5 in the South Korean context by using an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Additionally, it aimed to expand on the psychometric properties that were previously established through the English, French, and Arabic versions of the DAR-5 in a community-dwelling sample of South Korean adults who experienced traumatic events. Thus, the internal reliability and concurrent validity of the DAR-5-K were evaluated. The concurrent validity of the scale was examined in relation to other variables, including PTSD symptoms, depression, anxiety, and self-esteem. Lastly, the present study aimed to apply a cut-off score of 12 on the DAR-5—scores above which have been reported to demarcate problematic anger in previous studies—to the South Korean adult sample.In the present study, a nationwide anonymous online survey was conducted in 2018 according to the South Korean population census standard. The survey accounted for socio-demographic factors such as age, gender, and residential area. A total of 1,657 individuals participated in the survey, and 1,137  of them completed the survey. Of these 1,137, those who had not experienced a traumatic event (based on DSM-5 diagnostic criteria) (N = 323) were excluded. Therefore, the final sample consisted of 814 South Korean adults who had experienced traumatic events. All the participants were native Korean speakers.SD = 9.56). There were 174 participants who were in their 20s (21.4%), 221 in their 30s (27.1%), 270 in their 40s (33.2%), and 149 in their 50s (18.3%). In all, 421 (51.7%) participants were male and 393 (48.3%) were female. More than half of the respondents  resided in Seoul and its surrounding metropolitan area. In terms of marital status, the participants were categorized as either married , single , and divorced/bereaved and others . Most participants were employed . Housewives , professional workers , self-employed individuals , college students , and unemployed/other individuals  were also present in the sample. The participants marked one of the following categories as their educational level: middle school , high school , college , and graduate . The participants were categorized into two groups based on their monthly income. Among them, 397 (48.8%) earned less than the average monthly income of South Korean laborers , 394 (48.4%) earned equal to or more than it, and 23 (2.8%) chose ‘not applicable’.The mean age of the final sample (N = 814), which consisted of individuals ranging from 20 to 55 years of age, was 39.41 years  and DigiCert security service—a digital certificate association that provides services certified by ISO 9001—to ensure security and the encryption of surveys. All responses from the survey were collected with encrypted secure sockets layer (SSL) connections to ensure the secure transmission of sensitive data. Users created their own passwords and entered their own username and password to log in, which ensured user authentication. The survey included questions that queried participants’ socio-demographic characteristics as well as multiple instruments for examining the sociological and psychological constructs of the respondents. The survey took approximately 20 min to complete. The participants were given some amount of online credit points as compensation. The present study was approved by the Institutional Review Board (IRB) of the university that the researchers are affiliated with.Five instruments were used in order to cross-culturally validate the DAR-5. They are listed below.The LEC-5  assessesnone of the time, 2 = a little of the time, 3 = some of the time, 4 = most of the time, 5 = all of the time), and the total scale score ranges from 5 to 25. Higher scores indicate a worse symptomatology of anger. Examples of items include, “I found myself getting angry at people or situations” and “When I got angry at someone, I wanted to hit them.” The DAR-5 was first translated from English to Korean by some bilingual researchers, including a psychology professor affiliated with a University in the United States. The instrument was then back-translated into English by a South Korean professor of Counseling Psychology Education and three researchers—one of whom was in a Ph.D. program and two of whom were in a master’s program in the field of Psychology. The research team members compared the back-translated version with the original English version of the scale until all the researchers reached a final agreement. A readability test of the DAR-5-K was conducted with high-school students and teachers in 2015. Their feedback was incorporated until there were no complaints regarding difficulties in reading or comprehending the items, and this finalized version of the DAR-5-K was used in the present study.The DAR-5  is compoThe original English version of the DAR-5 has been found to have good internal consistency with a Cronbach’s α of 0.89 to 0.90 . AdditioThe Primary Care PTSD Screen for DSM-5 (PC-PTSD-5)  is a 5-iAs per the cut-off score of 3 that was determined in the validation study of the PC-PTSD-5  and confThe Brief Symptom Inventory-18 (BSI-18)  is an 18The Rosenberg Self-esteem Scale  is a 10-814 participants were randomly divided into two data sets. One for EFA and another for CFA. EFA was first performed with a randomly selected half of the overall sample (N = 405), using maximum likelihood factoring with direct oblique rotation to examine the factor structure of the DAR-5-K. IBM SPSS Statistics 21.0. was used. First, the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity were used to evaluate the sampling adequacy. Scree plot and factor interpretability were used to determine the number of factors, and factor loadings equal to or more than 0.40 were considered acceptable. Then, CFA with the other half of the sample (N = 409) was conducted with Mplus 8.0 to test the one-factor structure of the DAR-5-K items. Error terms were set to be uncorrelated. The CFA result was further validated with two additional samples, including the higher PTSD symptoms group, and the no/lower PTSD symptoms group. Since chi-square —another measure of internal reliability was calculated. Finally, McDonald’s coefficient omega (ω)—an additional indicator of internal reliability that performs better than Cronbach’s alpha and is thus preferred when tau-equivalence is violated —was alsoectively . A t-tesectively . In the ectively .SE = 0.086) and a kurtosis value of -0.479 (SE = 0.171). Other scales also followed a normal distribution; PC-PTSD-5 had a skewness value of 0.779 and a kurtosis value of -0.602; depression had 0.519 and − 0.507 skewness and kurtosis, respectively; anxiety had 0.729 and − 0.372 skewness and kurtosis, respectively; and lastly, self-esteem had a skewness value of -0.159 and a kurtosis value of -0.274.The means and standard deviations for the DAR-5-K, PC-PTSD-5 (PTSD symptoms), BSI-18 , and RSE (self-esteem) are presented in Table Studies of the original English version , French mentclass2pt{minimdf = 5), p = .3910, CFI = 1.000, TLI = 1.000, SRMR = 0.009, and RMSEA = 0.010. For further validation of the CFA findings, the one-factor model was retested with two additional samples, including the higher PTSD symptoms group (N = 216) and no/lower PTSD symptoms group (N = 598). The higher PTSD symptoms group showed an acceptable model fit of df = 5), p = .0487, CFI = 0.981, TLI = 0.963, SRMR = 0.024, and RMSEA = 0.105 (N = 216), and the no/lower PTSD symptoms group showed an acceptable model fit ofdf = 5), p = .0494, CFI = 0.993, TLI = 0.985, SRMR = 0.016, and RMSEA = 0.064. The standardized factor loadings for the five DAR-5-K items are presented in Fig. The CFA for a one-factor model showed a satisfactory model fit, with a entclass1pt{minimaThe DAR-5-K was shown to have a good internal consistency, as demonstrated by a Cronbach’s α of 0.893 to 0.907, CR of 0.893 to 0.907, and McDonald’s coefficient omega (ω) of 0.894 to 0.907. Excellent item-total correlations were observed with the DAR-5-K, with correlations ranging from 0.825 to 0.876 in the overall sample, 0.815 to 0.866 in the higher PTSD symptoms group, and 0.802 to 0.882 in the no/lower PTSD symptoms group. Inter-item correlations of the scale seemed adequate, ranging from 0.613 to 0.756 in the overall sample, 0.503 to 0.684 in the higher PTSD symptoms group, and 0.553 to 0.758 in the no/lower PTSD symptoms group. The PC-PTSD-5 (PTSD symptoms), BSI-18 , and RSE (self-esteem) also showed good internal consistency in the overall sample with Cronbach’s alphas of 0.752, 0.905, 0.920, and 0.831, respectively.Results of a Multigroup CFA across PTSD symptoms severity indicated that a metric invariance of the DAR-5-K was achieved. Fit indices of the configural model of the DAR-5-K were ested by .t = 9.517, p < .001), PTSD symptoms , depression , and anxiety , and lower scores with respect to self-esteem  compared to individuals in the no/lower PTSD symptoms group. The higher and no/lower PTSD symptoms groups differed with a medium to large effect size (Cohen’s d from 0.64 to 3.95). A large effect size was found for PTSD symptoms (d = 3.95), depression (d = 0.82), and anxiety (d = 0.94), and a medium effect size was found for anger reactions (d = 0.78) and self-esteem (d = 0.64).As presented in Table Correlations between the DAR-5-K total scores and the depression, anxiety, and self-esteem scores are presented in Table t = 9.913, p < .001), depression , and anxiety , and lower scores for self-esteem . The high and low DAR-5-K subsamples differed with a medium to large effect size (Cohen’s d from 0.68 to 1.73). A large effect size was found for depression (d = 1.70) and anxiety (d = 1.73), and a medium effect size was found for PTSD symptoms (d = 0.72) and self-esteem (d = 0.68).Table r = .825 to r = .876), and moderate inter-item correlations (r = .613 to r = .756) in the overall sample. In this study, a one-factor structure of the scale was confirmed, as reported in the English, French, and Arabic validation studies of the DAR-5.Anger is one of the commonly reported symptoms among populations that have experienced traumatic events. Despite the numerous traumatic events that have occurred in recent years in South Korea and the presence of research on the anger that emerges due to such traumatic events, a concise and accessible measure of anger has yet to be validated in the South Korean setting. Therefore, the present study conducted the South Korean validation of the DAR-5, and demonstrated the DAR-5-K to be a concise, valid, and reliable measure for screening anger among South Korean adults who have had traumatic experiences. Comprising only five items, the DAR-5-K demonstrated good internal reliability , good item-total correlations (df = 5), CFI = 1.000, TLI = 1.000, SRMR = 0.009, RMSEA = 0.010). However, the RMSEA score (0.105) of the higher PTSD symptoms group revealed the need for further validation and item refinement. This indicated that the nature of anger demonstrated in the higher PTSD symptoms group could have been distorted by their experience of PTSD. In general, anger is a normal and natural response to a certain situation. However, PTSD can add another layer to one’s emotion as the function of one’s anger may be widely different depending on the context of one’s PTSD. This poor model fit with the higher PTSD symptoms group can also be cultural. Identifying possible reasons that contribute to the poor model fit is beyond the scope of the present study. However, these results warrant future study.In this study, the CFA results demonstrated the replicability of the factor structure of the original DAR-5 with five items. A CFA with the DAR-5-K showed an excellent model fit with an overall sample in measuring the anger construct . Based Concurrent validity was established based on the result that individuals in the higher PTSD symptoms group displayed significantly higher levels of anger reactions, depression, and anxiety than those in the no/lower PTSD symptoms group. This result replicated the findings obtained from the original validation study of the DAR-5 where significant differences in the levels of anger reactions on the DAR-5 between groups of individuals with high and low PTSD symptoms were found . This reThe concurrent validity of the DAR-5-K was also supported by the significant correlations of anger reactions with depression and anxiety in the overall sample, higher PTSD symptoms group, and no/lower PTSD symptoms group. Again, these results replicated the findings obtained during previous validation studies of the DAR-5 , 29. FurForbes et al.  determinThe current validation study thus applied the cut-off point determined in these previous validation studies , 29 to tThe aforementioned findings related to reliability, factor structure, concurrent validity, and the results with a cut-off point for the DAR-5-K demonstrate that the scale is a reliable and valid tool to measure problematic anger following traumatic events. This suggests that the results of psychometric properties of the DAR-5 reported in previous validation studies , 29, 30 However, these conclusions should be taken into consideration with cognizance of the limitations of the present study. The present study categorized the overall sample into two groups, namely the higher and no/lower PTSD symptoms groups, which were divided based on a cut-off point of 3 on the PC-PTSD-5—an instrument for posttraumatic stress symptoms. However, a clinical sample diagnosed with psychopathologies was absent in the current study. The use of a clinical sample and implementation of diagnostic tools for psychiatric illnesses including PTSD, depression, and anxiety in future studies would strengthen the results of the current study. Further, in addition to the self-report measures used in the present study, the validity of the DAR-5-K could be improved through future research where observational reports of behavioral problems associated with anger are documented and a structured interview for the psychiatric disorders related to anger is administered.Moreover, unlike the original English and French validation studies of the DAR-5, the present study does not use the STAXI-2 as a supplementary tool to confirm the cut-off score of 12. The use of the STAXI-2 with an established cut-off score was not possible as a validation study of the STAXI-2 has not yet been performed in the South Korean context. The current study did not make use of the South Korean version of the STAXI-1 (STAXI-1-K) either. This was because, to the best of our knowledge, a cut-off point for the STAXI-1-K has not been established. Further, given the number of items in the STAXI-1 (44 items) and STAXI-2 (57 items), an addition of a lengthy questionnaire would have significantly increased the cognitive burden on participants. This would consequently increase the survey dropout rate, as suggested by . TherefoIn conclusion, the present study indicates that the DAR-5-K has robust psychometric properties and is a shortened brief and effective measure to evaluate the presence of problematic anger in the South Korean adult population who has been exposed to traumatic events. The generalizability of the results obtained by using the DAR-5 in the previous validation studies , 29, 30


Several approaches have been developed to analyze the entry of highly pathogenic viruses. In this study, we report the implementation of a Bimolecular Multicellular Complementation (BiMuC) assay to safely and efficiently monitor SARS-CoV-2 S-mediated membrane fusion without the need for microscopy-based equipment. Using BiMuC, we screened a library of approved drugs and identified compounds that enhance S protein-mediated cell-cell membrane fusion. Among them, ethynylestradiol promotes the growth of SARS-CoV-2 and Influenza A virus in vitro. Our findings demonstrate the potential of BiMuC for identifying small molecules that modulate the life cycle of enveloped viruses, including SARS-CoV-2.The online version contains supplementary material available at 10.1186/s12985-023-02068-1. The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) , 2 has c0). Proteolytic activation of S0 by host proteases (i.e. TMPRSS2) produces the mature pre-fusion S protein incorporated in the virions  was kept at ≤ 1%. Statistical analysis was done with Libre Office Calc.For the identification of small molecules with the ability to modulate membrane fusion, the protocol was adjusted to meet the necessary screen standards. To evaluate the robustness of our assay, we calculated the Z′-factor and the Signal-to-Noise (S/N) ratio. To study the effect on the membrane fusion induced by the Nipah virus, we carried out the original BiMuC assay described in . In this4 cells/plate) in an opaque white 96-well plate and treated with the appropriate compound at the indicated concentration for 48 h. Next, 100 µl of CellTiter-Glo® reagent was added and let the luminescence signal stabilizes for 10 min. The luminescence was recorded on a VictorX multi-plate reader .The toxicity of the compounds was evaluated with Cell-Titer Glo (Promega) following the manufacturer’s indications. Briefly, cells were seeded (~ 1 × 1050) method in MDCK cells. In all assays, [DMSO] was kept at ≤ 1%.Vero E6 cells were infected at a multiplicity of infection (MOI) of 0.01 with SARS-CoV-2. Next, ethaverine, rabeprazole, or ethynylestradiol were added at the appropriate concentration. Additionally, cells were treated with DMSO. After 48 h, supernatants were collected and titered by standard plaque assay in VeroE6 cells. Alternatively, MDCKs cells were infected with IAV/WSN/33 at an MOI of 0.001. Samples were incubated in the absence or presence of ethaverine, rabeprazole, or ethynylestradiol at the indicated concentrations for 48 h. Next, supernatants were collected and titered by the 50% Tissue Culture Infective Dose  protocols. These procedures should also be safe in the case of highly pathogenic viruses, such as SARS-CoV-2. To demonstrate that the SARS-CoV-2 BiMuC assay could be used in high-throughput conditions, we set up an assay to identify small molecules that influence the SARS-CoV-2 S-mediated membrane fusion process. We were particularly interested in small molecules that could enhance the viral entry. Despite introducing new vaccine platforms, vaccine production often relies on producing large quantities of virus. Accordingly, various strategies have been implemented to increase viral production, from cell selection to optimizing replication procedures –17. A moin trans, we intentionally did not include TMPRSS2 in our assay to set up a sub-optimal fusion state that facilitates the identification of small molecules that enhance syncytia formation. Speeding up or increasing virus production would expedite vaccine development, reduce production costs and increase manufacture yield [Our assay is based on Hek 293T cells, which express low levels of ACE2 and TMPRSS2 , 19. Whire yield . To confNext, we adapted our methodology for the HTS of small molecules and verified its suitability by the Z′ factor  . BrieflyZ-Score; 76 primary hits were selected based on a Z-Score > 1.5  was screened, and the results were standardized by the .5 Table . Hits weA total of 6 compounds were selected; these were repurchased from a different vendor and re-tested at multiple concentrations to confirm their ability to enhance viral membrane fusion in Hek 293T. Compounds were incubated for 48 or 72 h . Additionally, the toxicity of ethynylestradiol was tested in the presence of TMPRSS2  clearly showed toxicity above the rest of the compounds. Based on these results (effectiveness and toxicity) we decided to focus on ethaverine, rabeprazole, and ethynylestradiol from this point on.We also assayed the toxicity of these 6 compounds by measuring the ATP content in the cells  on the entry of enveloped viruses, we turned our attention to the Nipah virus (NiV). NiV, a member of the Paramyxoviridae family, requires two proteins to carry out the fusion of the viral and cellular membranes, the glycoprotein (G) that attaches to a receptor protein on the surface of the receptor cell and the fusion (F) protein which in turn will force the fusion of the cellular and viral membranes. NiV is a BSL4 restricted agent. Therefore, to test the effect of ethynylestradiol, ethaverine, and rabeprazole we utilized the BiMuC assay adapted to the study of NiV entry . Our res50 . IAV causes substantial annual morbidity and mortality with seasonal epidemic outbreaks. Seasonal influenza epidemics may be managed by vaccination, and multi-valent vaccines containing IAV and influenza B virus components are widely distributed annually. Like SARS-CoV-2, IAV is an enveloped virus that relies on a class I fusion protein, the hemagglutinin (HA) protein, for the fusion of the viral and cellular membranes.IAV infections we carried out in Madin-Darby canine kidney (MDCK) cells in the presence and absence of the selected compounds . Our resMost of the compounds included in the chemical library used in our screen are approved drugs and are currently used in several treatments. However, it is important to note that our experiments are insufficient to claim that the hit compounds, including ethynylestradiol, might worsen the outcome of a patient during infection with an enveloped virus.In summary, our results suggest that ethynylestradiol could be used in vitro to enhance the growth of enveloped viruses. Based on previous works –29, ethyOur results demonstrate that BiMuC can be implemented to monitor the SARS-CoV-2 membrane fusion process. Furthermore, we show that this methodology is suitable for the high-throughput identification of small molecules that influence the viral entry of some enveloped viruses and, thus, viral growth.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Figure S1. SARS-CoV-2 S protein-induced syncytia. Hek 293T cells were transfected with SARS-CoV-2 S protein and human ACE2 coding plasmids (A-C) or mock transfected (D). After 24 hours cells were visualized using a Motic AE200 microscope with a 10x objectiveFigure S2. Ethynylestradiol toxicity in the presence of TMPRRS2. A and B. To analyze the effect of TMPRSS2 on ethynylestradiol toxicity Hek 293T cells were transfected with TMPRR22 or eYFP and 48 hours later the toxicity was assayed. Toxicity was analyzed by measuring ATP levels using the Cell-Titer Glo assay (Promega) following the manufacturer’s indications. Dots represent the average of two independent experiments. The CC10, CC50, and CC90 values are indicated


Biocompatible particle-stabilized emulsions have gained significant attention in the biomedical industry. In this study, we employed dynamic high-pressure microfluidization (HPM) to prepare a biocompatible particle emulsion, which effectively enhances the thermal stability of core materials without the addition of any chemical additives. The results demonstrate that the HPM-treated particle-stabilized emulsion forms an interface membrane with high expansion and viscoelastic properties, thus preventing core material agglomeration at elevated temperatures. Furthermore, the particle concentration used for constructing the emulsion gel network significantly impacts the overall strength and stability of the material while possessing the ability to inhibit oxidation of the thermosensitive core material. This investigation explores the influence of particle concentration on the stability of particle-stabilized emulsion gels, thereby providing valuable insights for the design, improvement, and practical applications of innovative clean label emulsions, particularly in the embedding and delivery of thermosensitive core materials. High internal phase emulsion (HIPE), characterized by a dispersed phase volume fraction higher than 74%, has gained significant popularity across industries such as chemicals, food, pharmaceuticals, and petrochemicals. This innovative emulsion system offers great potential for the creation of advanced materials . These mThere has been a growing interest in the application of particle emulsifiers for stabilizing emulsions, particularly those composed of hard inorganic particles like titanium dioxide  and siliCurrently, the preparation of certain highly internal phase emulsions (HIPEs) still relies on the use of crosslinkers or organic solvents that do not meet clean label standards. These substances may persist in the emulsion products, severely limiting their applicability in the fields of food, pharmaceuticals, and health products. Hence, it is imperative to discover a more energy-efficient, environmentally friendly, and uncomplicated method to produce particle emulsifiers that are suitable for clean label applications, which presents a significant challenge. These particle emulsifiers possess exceptional interfacial properties, ensuring high safety and suitability for large-scale industrial production and commercialization . One proThis study utilized milk protein concentrate (MPC) enriched with casein micelles as the emulsifier for the particles. It is worth noting that casein micelles possess a highly hydrated porous structure with a complex hierarchical arrangement ,20,21,22MPC485  was purchased from Fonterra Co., Ltd. . Conjugated linoleic acid (CLA) was purchased from Toole Biotechnology Co., Ltd. . All the other chemicals of analytical grade were from Sinopharm Chemical Reagent Co., Ltd. . All the solutions were prepared using ultrapure water .2-casein, αs1-casein, and β-casein, with a ratio 1.3:1:4:4 [MPC is composed of whey proteins and caseins in a ratio of 1:4. Whey protein is mainly composed of β-lactoglobulin (β-Lg) and α-lactalbumin (α-La). Casein is mainly composed of four types of monomers, κ-casein, αs.3:1:4:4 . These m.3:1:4:4 . In addi.3:1:4:4 . The MPCThe MPC dispersion (5% weight percentage) was preheated to 50 °C and then subjected to three cycles of HPM using a high-pressure homogenizer equipped with a Z-shaped module containing microchannels with a diameter of 75 μm . Each HPM treatment was conducted at 90 MPa. The temperature of the MPC dispersion at the outlet of the homogenizer was approximately 60 °C. Three replicates were prepared for each MPC dispersion treated with HPM at 90 MPa, labeled as MPC-HPM90. The untreated MPC dispersion served as the control and was labeled as MPC-0.w/v) was added to the CLA. First, 10 mL of protein dispersion was accurately measured and transferred into a 120 mL plastic beaker. Subsequently, 30 mL of CLA was slowly introduced to the aqueous phase using a constant flow pump, while high-speed shearing dispersion at a speed of 10,000 rpm (~4785× g) was conducted for 1 min with 10 s pauses using the high-speed disperser . It is worth noting that MPC is a multi-component mixture, and the particles mentioned in the article refer to the particle emulsifier. The resulting high internal phase emulsion gels (HIPE-gels) were obtained. The experiment was performed at a controlled temperature of 25 °C, utilizing a constant temperature water bath. Three replicates of the HIPE-gels were prepared. For storage, the HIPE-gels were maintained under controlled conditions of 45 °C in constant temperature shaking incubators within a light-free environment. The stability and lipid oxidation of the HIPE-gels were analyzed during a 14-day high-temperature storage period. Additionally, the samples were stored for an extended period of 7 weeks at 45 °C, allowing for visual assessment of their appearance and determination of shelf life. Approximately 8 g of the samples were stored in 10 mL glass vials for visual observation. For measuring other indicators during the storage period, 30 mL wide-mouth plastic dishes with covers were used.HIPE-gels were prepared with the oil phase volume fraction of 75%, utilizing conjugated linoleic acid (CLA) as the core material (oil phase) and an aqueous dispersion containing 2 or 4 wt% MPC  as the aqueous phase. To facilitate visualization of the CLA using both visual and microscope observation, Nile red dye  equipped with a 10× objective magnification and an excitation wavelength of 552 nm.A rotational rheometer  with a parallel plate having a diameter of 25 mm and a measurement gap of 1 mm was employed for the rheological analyses. The angular frequency was set to 10 rad/s, and amplitude scanning was conducted within a shear strain range of 0.01% to 1000%. Additionally, creep experiments were performed on the HIPE-gels by applying a stress of 10 Pa to the sample for 3 min, followed by a relaxation period of 3 min. The entire duration of changes in shear strain was recorded.2 solution used was freshly prepared. Freshly prepared or stored emulsion gel (0.1 g) was placed in a 15 mL centrifuge tube. A mixture of 3 mL of chloroform–methanol solution  was then added, and the mixture was vortexed for 30 s. The solution was mixed thoroughly for 20 min and then centrifuged at 3000× g for 10 min at room temperature. Precisely measured 1 mL of organic phase was then added to a 10 mL brown flask, followed by the addition of 50 μL of FeCl2 solution (3.5 g/L). The flask was filled with a chloroform–methanol solution , and 50 μL of KSCN solution (300 g/L) was added after mixing. After 5 min at room temperature, the absorbance of the solution was measured at 500 nm. The POV was calculated using Equation (1):X is the content of peroxides in the sample (meq/kg), c is the mass of iron in the sample obtained from the standard curve (μg), m is the mass of sample (g), 55.84 is the atomic weight of iron, and 2 is conversion factor.The stability of lipid oxidation was investigated by measuring the peroxide value (POV) of CLA in HIPE-gels based on the method of Zhuang et al. . The FeC® stability analyzer [The influence of a high temperature and centrifugal force on the physical stability of the O/W HIPE-gels was assessed using a LUMiSizeranalyzer . The gelp-value of less than 0.05, indicating significant differences between the samples.The results were expressed as the mean ± standard deviation. The statistical analyses was performed using IBM SPSS Statistics 25 . An analysis of variance (ANOVA) was conducted, followed by Duncan’s test at a 95% confidence level. Statistical significance was determined at a The prepared HIPEs were carefully placed in glass bottles and inverted for observation, as depicted in The HIPEs were formulated using two different concentrations  of MPC. These MPC concentrations tightly adhered to the glass bottles, exhibiting gel-like properties upon inversion, signifying the successful preparation of the HIPE-gels. In contrast, the HIPE-gels prepared using whey protein microgels with concentrations ranging from 2.5% to 7.5% exhibited low flowability, which was also observed in ref. , causingTo further investigate the influence of MPC particle concentration on the performance of the HIPE-gels, we employed confocal laser scanning microscopy (CLSM) to observe their microstructures. Furthermore, ImageJ software was utilized to analyze the droplet size and distribution based on the CLSM images obtained. As depicted in The particle concentration of the MPC played a significant role in determining the size, morphology, and size distribution of the oil droplets in both the freshly prepared and stored HIPE-gels. In the HIPE-gels fabricated using a low concentration (2 wt%) of MPC, the oil droplets were larger, irregularly distributed, and displayed uneven shapes. Specifically, the average size of the oil droplets in the HIPE-gels prepared with MPC-0 was measured at 12.85 ± 5.68 μm, while those prepared with the MPC-HPM90 were slightly smaller, with an average size of 10.53 ± 2.52 μm. By increasing the MPC concentration to 4 wt%, the size of the oil droplets in the HIPE-gels decreased, and they assumed an irregular circular shape. The average size of the oil droplets in the HIPE-gels prepared using MPC-0 was 6.42 ± 1.21 μm, while those prepared using MPC-HPM90 exhibited an average size of 5.84 ± 1.45 μm. This relationship between particle concentration and droplet size can be attributed to the insufficiency of protein-based particle emulsifiers at lower concentrations, which fail to adequately cover the oil droplet surface during the shear dispersion process, resulting in droplet coalescence.Additionally, the use of MPC with or without HPM treatment for HIPE-gel preparation influenced the morphology, size, and size distribution of the oil droplets. The incorporation of MPC-HPM90 as an emulsifier in the HIPE-gel formulation led to a noticeable improvement in the homogeneity of smaller oil droplets. This enhancement can be attributed to the increased deformability and collapsibility of the particles. The impact of this effect was most prominent in samples with lower concentrations (2 wt%), as evident from Following a two-week storage experiment at 45 °C, the concentration of the protein-based particle emulsifiers emerged as a crucial factor affecting the stability of the HIPE-gels. In the HIPE-gels stabilized using a low concentration (2 wt%) of MPC, severe droplet coalescence and enlargement were observed, resulting in large oil droplets reaching approximately 100 μm. Specifically, the average size of the oil droplets in the HIPE-gels prepared using MPC-0 was measured at 28.74 ± 10.77 μm, while those prepared using MPC-HPM90 exhibited a smaller average size of 16.09 ± 6.43 μm. Conversely, the droplet size experienced minimal changes during storage in the HIPE-gels stabilized with MPC-HPM90 at a high concentration (4 wt%), whereas some oil droplets aggregated in the HIPE-gels stabilized with MPC-0 . The aveThe rheological properties of emulsions have a significant impact on their stability and practical applications. In order to gain a comprehensive understanding of the rheological properties of HIPE-gels and evaluate their practical utility, we conducted experiments on HIPE-gels prepared with different concentrations of MPC to examine their stability under large amplitude oscillatory shear. The dynamic viscoelastic properties of the HIPE-gels were assessed using creep recovery tests, as depicted in The strain value represents the cumulative deformation experienced by the gel sample under stress. As this value increased, the stiffness of the gel samples noticeably decreased, while a decrease in strain corresponded to an increase in gel hardness. Hence, the strain value serves as an indicator of gel firmness. When subjected to constant stress, the HIPE-gels stabilized using MPC-0 displayed higher strains compared to those stabilized using MPC-HPM90. This discrepancy suggests that the gels stabilized using MPC-0 were more susceptible to deformation due to their fragile gel structure, rendering them vulnerable to external perturbations. Conversely, the HIPE-gels stabilized using MPC-HPM90 exhibited minimal deformation when exposed to external force. This remarkable resistance to deformation can be attributed to the formation of interfacial membranes by deformed particles subsequent to the HPM treatment. MPC-HPM90 may optimize the benefits of both soft and hard particles when placed at the interface. Softer particles  can spontaneously adsorb on the interface, while harder particles  have limited adaptability ,33. SoftConjugated linoleic acid (CLA) is a type of polyunsaturated fatty acid  that undThe peroxide value (POV) of pure and unprotected CLA was determined to be 25.00 ± 0.88 meq/kg oil. However, the POV of CLA increased when incorporated into the freshly prepared HIPE-gels. Notably, the oxidation degree of CLA was more pronounced in the HIPE-gels stabilized with 2 wt% MPC. The POV of CLA in the HIPE-gels stabilized using MPC-0 and MPC-HPM90 was measured at 35.60 ± 0.84 meq/kg oil and 37.14 ± 2.51 meq/kg oil, respectively. In the HIPE-gels stabilized with freshly prepared 2% MPC, regardless of whether MPC-0 or MPC-HPM90 was used, insufficient protein concentration resulted in incomplete coverage of the oil droplet surfaces. Consequently, lipid oxidation occurred during the approximately 20-min HIPE-gel preparation process. The level of lipid oxidation was similar in both samples, as depicted in After 14 days of storage, the POV of pure CLA significantly increased to 36.69 ± 1.41 meq/kg oil. Meanwhile, the HIPE-gels stabilized using 2 wt% MPC exhibited an acceleration in peroxide production, as evident from the POV of CLA. In fact, the POV of CLA in the HIPE-gels stabilized using MPC-0 and MPC-HPM90 reached 47.71 ± 1.62 meq/kg oil and 43.88 ± 0.66 meq/kg oil, respectively. As the particle emulsifier concentration increased, the HIPE-gels effectively suppressed the lipid oxidation of CLA. Our findings reveal an intriguing phenomenon of inhibited CLA oxidation in response to an increased particle emulsifier concentration. This observation can be attributed to the three-dimensional network formed during gelation in HIPE-gels, which restricts the diffusion and transfer of oxygen, pro-oxidants, and free radicals to the O–W interface ,39. ConsTo assess the physical stability of the HIPE-gels, we utilized an accelerated centrifugation technique using LUMisizer® stability analysis . This meIn The majority of the initial profile lines  at different concentrations (c = 0.1–2.0 wt%). The results indicated that HIPEs with high particle concentrations (c = 1.0–2.0 wt%) exhibited a relatively high physical stability, with an extremely slow creaming rate during centrifugation and a final value not exceeding 0.025.We observed that the HIPE-gels stabilized with lower concentrations (2 wt%) of MPC demonstrated a significant increase in transmittance near the bottom of the sample tube A,B, indiu et al.  assessedu et al.  monitoreMoreover, the HIPE-gels stabilized with MPC-HPM90 displayed less variation in transmission intensity at the same concentration. Among all the samples, the HIPE-gel stabilized using 4 wt% MPC-HPM90 exhibited minimal changes under accelerated centrifugation, indicating its high stability against coalescence, even in the presence of centrifugal force. Notably, the MPC-stabilized HIPE-gels demonstrated considerable resistance to droplet coalescence. Despite water seepage from the gel structure, no Nile red-stained oil droplets were released from the emulsion. Theoretical principles suggest that applying centrifugal force to a mixture of oil and water would result in phase separation, with the oil phase rising to the top. However, the HIPE-gels we prepared exhibited exceptional physical stability owing to the O–W interfacial membrane’s high mechanical strength, which was constructed by soft particles.This study investigated the impact of the particle concentration of MPC modified with HPM on their own structure and thermal stability during storage, as well as its effect on the thermal stability of the encapsulated core materials. The results indicate that at lower concentrations (2%), the MPC particles failed to provide sufficient coverage to the oil droplets, resulting in a fragile network structure and increased possibility of oil droplet coalescence. In contrast, higher concentrations (4 wt%) of MPC demonstrated a notable ability to maintain long-term stable oil droplets. Specifically, the HIPE-gels stabilized using MPC-HPM90 exhibited insignificant changes in oil droplet size, size distribution, and gel network strength during a 14-day high-temperature storage period. These gels displayed excellent physical stability in elevated temperature environments and effectively inhibited lipid oxidation of the core material. This study provides valuable experimental data and theoretical guidance for the development, refinement, and application of particle emulsifiers. The research findings pave the way for the creation of more efficient emulsifiers that are suitable for use in environmentally friendly labeled consumer products. In summary, we have successfully developed HIPE-gels, a novel material that can enhance the thermal stability of heat-sensitive CLA without requiring any additional chemical emulsifiers. In future studies, we will investigate the feasibility of using HIPE-gels as a template for the preparation of porous materials and to polymerize lenoic acid. This technology has the potential to offer significant advantages over traditional emulsion polymerization processes, which use toxic surfactants that can have harmful environmental and health implications.